Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/13341
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Antonio, N. | - |
dc.contributor.author | de Almeida, A. | - |
dc.contributor.author | Nunes, L. | - |
dc.date.accessioned | 2017-05-15T09:00:20Z | - |
dc.date.available | 2017-05-15T09:00:20Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 2182-8458 | - |
dc.identifier.uri | http://hdl.handle.net/10071/13341 | - |
dc.description.abstract | Booking cancellations have a substantial impact in demand-management decisions in the hospitality industry. Cancellations limit the production of accurate forecasts, a critical tool in terms of revenue management performance. To circumvent the problems caused by booking cancellations, hotels implement rigid cancellation policies and overbooking strategies, which can also have a negative influence on revenue and reputation. Using data sets from four resort hotels and addressing booking cancellation prediction as a classification problem in the scope of data science, authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%. This demonstrates that despite what was assumed by Morales and Wang (2010) it is possible to predict with high accuracy whether a booking will be canceled. Results allow hotel managers to accurately predict net demand and build better forecasts, improve cancellation policies, define better overbooking tactics and thus use more assertive pricing and inventory allocation strategies. | por |
dc.language.iso | por | - |
dc.publisher | Escola Superior de Gestão, Hotelaria e Turismo. Universidade do Algarve | - |
dc.relation | UID/MULTI/0446/2013 | - |
dc.rights | openAccess | por |
dc.subject | Data science | por |
dc.subject | Hospitality industry | por |
dc.subject | Machine learning | por |
dc.subject | Predictive modeling | por |
dc.subject | Revenue management | por |
dc.title | Predicting hotel booking cancellations to decrease uncertainty and increase revenue | por |
dc.title.alternative | Previsão de cancelamentos de reservas de hotéis para diminuir a incerteza e aumentar a receita | pt |
dc.type | article | - |
dc.pagination | 25 - 39 | - |
dc.publicationstatus | Publicado | por |
dc.peerreviewed | yes | - |
dc.journal | Encontros Científicos - Tourism and Management Studies | - |
dc.distribution | Internacional | por |
dc.volume | 13 | - |
dc.number | 2 | - |
degois.publication.firstPage | 25 | - |
degois.publication.lastPage | 39 | - |
degois.publication.issue | 2 | - |
degois.publication.title | Predicting hotel booking cancellations to decrease uncertainty and increase revenue | por |
dc.date.updated | 2019-04-01T12:48:30Z | - |
dc.description.version | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.doi | 10.18089/tms.2017.13203 | - |
dc.subject.fos | Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação | por |
dc.subject.fos | Domínio/Área Científica::Ciências Sociais::Outras Ciências Sociais | por |
iscte.identifier.ciencia | https://ciencia.iscte-iul.pt/id/ci-pub-37158 | - |
Aparece nas coleções: | ISTAR-RN - Artigos em revistas científicas nacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Post-print 1000-3980-1-PB.pdf | Versão Editora | 1,62 MB | Adobe PDF | Ver/Abrir |
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